It has always been a challenge with autonomous cleaning robots to ensure full coverage of a working area in a timely manner. Several efforts have been made to address this challenge, most of which are either too expensive or impractical for use in a typical consumer's daily application.
A cleaning robot's performance is measured by the amount of cleaning it does relative to the area that it visits in a given amount of time. If a cleaning robot relies purely on random movement, then its performance decreases relative to the amount of coverage as a function of time. In other words, efficiency (E) approaches zero when time (T) approaches infinity.
One way to optimize the robot's cleaning behavior is to equalize the cleaning rate with the coverage rate. To accomplish this, there is a need for a better method for the robotic device to keep continuous track of its own relative location in the workspace so that it can calculate and keep track of the paths that it has already cleaned. Different companies have worked on this matter in previous art, for example, the use of differential GPS, Ultrasonic transducers and scanning laser ranges. Each of the said technologies has its own limits and problems, such as expensive technology and complications of application.
Other solutions employ a navigation system that requires attaching large bar code targets at different positions in the workspace. In order to utilize the navigation system, however, the robot must see at least four of the bar codes simultaneously, which creates a significant operational problem.
Still other solutions utilize rotating laser rangefinders for localization and path determination. The use of rotating laser rangefinders on the robotic device has a few downsides, one of which is the mechanical nature of solution. Mechanical devices have a high tendency to break down and fail to function compared to fixed electronic devices. Another downside of this method is the price to employ the technology. Employing rotating laser rangefinders is a relatively expensive solution to the problem, considering that the overall result is not much better than employing random and simpler algorithms.
In solutions that use purely random navigation or random bouncing methods, focus has been on “sensing” the environment and making reactive decisions in response to the input from sensors. In previously proposed methods, the robot actively probed the environment and planned its movement accordingly. Although random methods are more practical and cheaper than the sophisticated technologies previously described, the robot's coverage is based on randomly selected paths, and the percentage of area cleaned and coverage rate are relative to the amount of time that the robotic device spends in the area. Robots using random movement patterns may not clean an acceptable percentage of the area during a finite time. Maximum cleaning efficiency is not achieved through random movement methods because there is too much unnecessary path overlap. An autonomous robot with a random movement method is unlikely to cover the entirety of an area in a finite amount of time.
As such, methods and systems for automated robotic movement with improved work-space coverage while balancing of finite operation time are provided herein.